The Sparks Foundation

April 2021

EDA by Ateendra Jha

#Importing the dictionarries 

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import scipy as scp
import plotly.express as px
import warnings
warnings.filterwarnings('ignore')

# Reading the Dataframe
MasterDF = pd.read_csv('SampleSuperstore.csv')
MasterDF.head()

	Ship Mode	Segment	Country	City	State	Postal Code	Region	Category	Sub-Category	Sales	Quantity	Discount	Profit
0	Second Class	Consumer	United States	Henderson	Kentucky	42420	South	Furniture	Bookcases	261.9600	2	0.00	41.9136
1	Second Class	Consumer	United States	Henderson	Kentucky	42420	South	Furniture	Chairs	731.9400	3	0.00	219.5820
2	Second Class	Corporate	United States	Los Angeles	California	90036	West	Office Supplies	Labels	14.6200	2	0.00	6.8714
3	Standard Class	Consumer	United States	Fort Lauderdale	Florida	33311	South	Furniture	Tables	957.5775	5	0.45	-383.0310
4	Standard Class	Consumer	United States	Fort Lauderdale	Florida	33311	South	Office Supplies	Storage	22.3680	2	0.20	2.5164

# Describing
MasterDF.describe()

	Postal Code	Sales	Quantity	Discount	Profit
count	9994.000000	9994.000000	9994.000000	9994.000000	9994.000000
mean	55190.379428	229.858001	3.789574	0.156203	28.656896
std	32063.693350	623.245101	2.225110	0.206452	234.260108
min	1040.000000	0.444000	1.000000	0.000000	-6599.978000
25%	23223.000000	17.280000	2.000000	0.000000	1.728750
50%	56430.500000	54.490000	3.000000	0.200000	8.666500
75%	90008.000000	209.940000	5.000000	0.200000	29.364000
max	99301.000000	22638.480000	14.000000	0.800000	8399.976000

MasterDF.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 13 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Ship Mode     9994 non-null   object 
 1   Segment       9994 non-null   object 
 2   Country       9994 non-null   object 
 3   City          9994 non-null   object 
 4   State         9994 non-null   object 
 5   Postal Code   9994 non-null   int64  
 6   Region        9994 non-null   object 
 7   Category      9994 non-null   object 
 8   Sub-Category  9994 non-null   object 
 9   Sales         9994 non-null   float64
 10  Quantity      9994 non-null   int64  
 11  Discount      9994 non-null   float64
 12  Profit        9994 non-null   float64
dtypes: float64(3), int64(2), object(8)
memory usage: 1015.1+ KB

MasterDF.shape

(9994, 13)

sns.pairplot(MasterDF)

<seaborn.axisgrid.PairGrid at 0x250a0c19ee0>

MasterDF['Postal Code'] = MasterDF['Postal Code'].astype('str')
MasterDF.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9994 entries, 0 to 9993
Data columns (total 13 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   Ship Mode     9994 non-null   object 
 1   Segment       9994 non-null   object 
 2   Country       9994 non-null   object 
 3   City          9994 non-null   object 
 4   State         9994 non-null   object 
 5   Postal Code   9994 non-null   object 
 6   Region        9994 non-null   object 
 7   Category      9994 non-null   object 
 8   Sub-Category  9994 non-null   object 
 9   Sales         9994 non-null   float64
 10  Quantity      9994 non-null   int64  
 11  Discount      9994 non-null   float64
 12  Profit        9994 non-null   float64
dtypes: float64(3), int64(1), object(9)
memory usage: 1015.1+ KB

def barp(df, varx, vary, hue):
    plt.figure(figsize=[20,10])
    sns.barplot(data = df, x=varx, y=vary, hue=hue, order= df[varx].value_counts().index)
    a = varx+ " vs "+ vary
    plt.title( a, fontsize=15 ) 
    plt.xticks(rotation=90)
    plt.show()    
def count(df, varx, hue):
    plt.figure(figsize=[30,10])
    sns.countplot(data = df, x=varx, hue=hue)
    a = 'Count of',varx
    plt.title(a, fontsize=15 )
    plt.xticks(rotation=90)
    plt.show()    
def scatterp(df, varx, vary, hue, alpha = 0.5):
    plt.figure(figsize=[20,10])
    sns.scatterplot(data = df, x=varx, y=vary, hue=hue, alpha=alpha )
    a = varx+ " vs "+ vary
    plt.title(a, fontsize=15 )
    plt.show()
def regp(df, varx, vary):
    plt.figure(figsize=[20,10])
    sns.regplot(data = df, x=varx, y=vary )
    a = varx+ " vs "+ vary
    plt.title(a, fontsize=15 )
    plt.grid(axis = 'y')
    plt.show()   
def jointp(df, varx, vary, alpha=0.5):
    plt.figure(figsize=[20,10])
    sns.jointplot(data = df, x=varx, y=vary)
    a = varx+ " vs "+ vary
    plt.title(a, fontsize=15 )
    plt.grid(axis = 'y')
    plt.show()

barp(MasterDF, 'Ship Mode', 'Sales', 'Segment')

barp(MasterDF[MasterDF.Sales > 4000], 'City', 'Sales', 'Segment')

barp(MasterDF[MasterDF.Sales > 4000], 'State', 'Sales',  'Segment')

barp(MasterDF, 'Category', 'Sales', 'Segment')

regp(MasterDF, 'Sales', 'Profit')

regp(MasterDF, 'Quantity', 'Sales')

regp(MasterDF, 'Quantity', 'Profit')

regp(MasterDF, 'Discount', 'Sales')

regp(MasterDF, 'Discount', 'Profit')

CorDF = MasterDF.corr(method ='pearson')

CorDF

	Sales	Quantity	Discount	Profit
Sales	1.000000	0.200795	-0.028190	0.479064
Quantity	0.200795	1.000000	0.008623	0.066253
Discount	-0.028190	0.008623	1.000000	-0.219487
Profit	0.479064	0.066253	-0.219487	1.000000

plt.figure(figsize=[10,10])
sns.heatmap(CorDF, annot=True)
plt.title('Correlation Among Various Variables')
plt.show()

MasterDF['Profit&Loss'] = ['Profit' if x > 0 else 'Loss' for x in MasterDF.Profit]

count(MasterDF, 'Ship Mode', 'Category')

count(MasterDF, 'Profit&Loss', 'Category')

sns.countplot(MasterDF['Ship Mode'], order= MasterDF['Ship Mode'].value_counts().index )

<AxesSubplot:xlabel='Ship Mode', ylabel='count'>

fig = px.scatter_3d(MasterDF, 'Sales', 'Discount', 'Profit')
fig.show()

---

Inferences

1. Most preferred shipment mode is Standard Class
2. Office Supplies are more profitable than any other categories.
3. Discunt has shown the inverse proportionality to the profit. So prefer to have less discount.
4. Even sales and discount has inverse relatiion that shows that sales are decreasing with high discounts.
5. Sales and profit have the posiive relation, which is obvoius.
6. in Sates, Florida has highest office supply sales while in city jacksonville is at top.